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• W2048281837 abstract "Time series are built as a result of real-valued observations ordered in time; however, in some cases, the values of the observed variables change significantly, and those changes do not produce useful information. Therefore, within defined periods of time, only those bounds in which the variables change are considered. The temporal sequence of vectors with the interval-valued elements is called a ‘multivariate interval-valued time series.’ In this paper, the problem of forecasting such data is addressed. It is proposed to use fuzzy grey cognitive maps (FGCMs) as a nonlinear predictive model. Using interval arithmetic, an evolutionary algorithm for learning FGCMs is developed, and it is shown how the new algorithm can be applied to learn FGCMs on the basis of historical time series data. Experiments with real meteorological data provided evidence that, for properly-adjusted learning and prediction horizons, the proposed approach can be used effectively to the forecasting of multivariate, interval-valued time series. The domain-specific interpretability of the FGCM-based model that was obtained also is confirmed." @default.
• W2048281837 created "2016-06-24" @default.
• W2048281837 creator A5058518771 @default.
• W2048281837 creator A5074015797 @default.
• W2048281837 date "2014-09-01" @default.
• W2048281837 modified "2023-10-03" @default.
• W2048281837 title "Evolutionary learning of fuzzy grey cognitive maps for the forecasting of multivariate, interval-valued time series" @default.
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• W2048281837 doi "https://doi.org/10.1016/j.ijar.2014.02.006" @default.
• W2048281837 hasPublicationYear "2014" @default.
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